PD6629

PD6629 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.

## Joining, by = "PDID"

	patient	ID	age_at_sample_exact	cell_type	phase	BaitLabel
3	PD6629	PD6629dc	55.05270	PB whole	Recapture	PD6629dc
4	PD6629	PD6629dd	58.83094	PB Gran	Recapture	PD6629dd
1	PD6629	COLONY60	60.26831	BFU-E-Colony	Colony	NA
5	PD6629	PD6629de	60.26831	PB Gran	Recapture	PD6629de
2	PD6629	COLONY62	62.28063	BFU-E-Colony	Colony	NA
6	PD6629	PD6629df	63.41136	PB Gran	Recapture	PD6629df
7	PD6629	PD6629dg	64.73374	PB Gran	Recapture	PD6629dg
8	PD6629	PD6629dh	65.86448	PB Gran	Recapture	PD6629dh

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.0000   -0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  17.8576  1.0236  17.4466  <.0001  15.8514  19.8637  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.

node	driver	status	child_count	type	colony_count	mean_lambda_rescaled	correction	sd_rescaled	lb_rescaled	ub_rescaled	median_rescaled	p_lt_wt
-1	WT	1	-1	local	1	17.85758	1.014746	1.0476692	15.918788	19.99907	17.82518	NA
63	DNMT3A	1	56	local	21	18.29717	1.014746	0.3236644	17.686498	18.95402	18.28683	0.346950
68	JAK2:DNMT3A	1	35	local	25	18.07328	1.014746	0.5992949	16.962819	19.31095	18.05629	0.426150
59	DNMT3A	0	1	local	1	20.30765	1.014746	2.8738357	14.401820	26.71867	20.04911	0.196000
58	CBL	0	1	local	1	14.78254	1.014746	2.6064727	9.282933	20.53121	14.90475	0.883925
88	9pUPD:JAK2:DNMT3A	0	2	local	2	19.01943	1.014746	1.3174011	16.620660	21.84153	18.94668	0.250200
76	TET2:JAK2:DNMT3A	1	8	local	8	17.20549	1.014746	0.9877071	15.345362	19.23166	17.17896	0.670825

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient	node	driver	child_count	lower_median	upper_median	lower_lb95	lower_ub95	upper_lb95	upper_ub95	N	group	age_at_diagnosis_pcy	max_age_at_sample	min_age_at_sample
PD6629	63	DNMT3A	56	0.0087466	7.263952	0.0061894	0.0222232	5.937797	8.782189	8	DNMT3A	54.8063	66.59274	55.78097
PD6629	68	JAK2	35	14.2541270	29.728295	12.6154093	15.9897441	27.776801	31.722219	8	JAK2	54.8063	66.59274	55.78097
PD6629	76	TET2	8	36.1591058	46.033123	34.3227129	37.9689969	44.237803	47.683728	8	TET2	54.8063	66.59274	55.78097
PD6629	88	9pUPD	2	32.8006950	46.316776	30.8979890	34.6854530	44.430877	48.241853	8	9pUPD	54.8063	66.59274	55.78097

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates

label	node	het.sensitivity	chr	start	end	nhet	nhom	mean_loh_event	lower_loh_event	upper_loh_event	t_before_end	t_before_end_lower	t_before_end_upper	kb	count_in_bin	count_se	pmut	pmut_se	xmean	xse_mean	xsd	x2.5.	x50.	x97.5.	xn_eff	xRhat	lmean	lse_mean	patient	driver3	child_count
LOH_9pUPD	88	0.9103	9	14690	33393006	0	2	43.59	37.76	46.24	2.736	0.08701	8.561	33300000	6654	81.57	0.01453	0.0001782	0.7977	0.001115	0.17	0.367	0.8425	0.9936	23273	1	3.42	0.0003039	PD6629	9pUPD:JAK2:DNMT3A	2

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots